The present disclosure relates generally to supply chain management, and, more particularly, to a demand forecast system and method with an adjustment mechanism based on a Grey forecast model.
In product supply, a supply chain supports material purchase, fabrication of materials into intermediate and finished products, and distribution of finished products to clients. Supply chain management has become important in meeting goals of reduced inventory, increased productivity, and enhanced competitiveness. Manufacturing and distribution facilities have limited resources and capacity, however, so not every client request may be met. For example, some requests may be promised but unfulfilled, some clients may experience inadequate supply, and other requests may be rejected. Consequently, effective capacity management in supply chain management, without excess capacity loss, has become important for product suppliers requiring control of manufacture or distribution.
In the supply chain, clients transmit demands to a supplier. The demand may include a request for a particular quantity of a device by a specific date. The supplier forecasts and plans its internal or external manufacturing schedule according to these received demands, and allocates capacity for product manufacture to satisfy each client. After receiving orders corresponding to demands from clients, the supplier begins manufacture of the products. It is understood that the supplier must invest capital to prepare related equipment and materials according to the demand forecast in advance. If the demand forecast is not close enough to the actual orders, the supplier will suffer significant losses.
Conventionally, the sales demand is forecast based on a regression or time series model. The regression model assumes the relationship between variables is linear. Actual orders, however, rarely present a clear trend because of uncertain market demand. The time series model requires a large number of experimental samples (always more than 50 or more). Additionally, the forecast result of the regression or time series model is an experimental simple forecast value with lack of flexibility, and thus, is not practical for use in a facility with various product types having unpredictable market lifetimes and limited historical forecast data, such as an upper stream and down stream factories in semiconductor industry.